Senior Data Scientist, Client Insights

Alloy is where you belong! 

Alloy solves the identity risk problem for companies that offer financial products by enabling them to outpace fraud and confidently serve more people around the world. Banks and Fintechs turn to Alloy to take control of fraud, credit, and compliance risk, and grow with the clearest picture of their customers.  

Through our values: Be Bold, Get Scrappy, Collaborate, and Celebrate Our Differences, we are creating a workplace where you can grow, thrive, and belong. See how we’ve been continuously recognized and named one of Inc.Magazine’s Best Workplaces, Forbes America’s Best Startup Employers, Best Fintech to Work for by American Banker, year after year.  

Check out our investors and read more about us here.  

About the team

The Client Insights team focuses on helping clients get the most value out of their data. We rely on analytics and data science to help clients improve their policies, better detect fraud, and stay up to date with industry best practices. The Client Insights team is made up of data scientists and data engineers who are working to deliver in-app tools that leverage client data to enhance the agent experience and quicker detect fraud.

What you’ll be doing

• Apply statistical and machine learning methods to build customer-facing models.
• Work closely with application engineers to operationalize models you've built, ensuring they meet rigors for customer usage, including model performance tracking and having mechanisms to retrain models.
• Take the initiative to innovate on our current models and apply new methodologies to new and existing problems/projects/products.
• Thought leadership around data governance and standardization
• Set standards for feature/variable definitions
• Produce documents that give visibility into the data pipelines you've built.
• Partner with engineering and product leads to provide guidance and leadership in roadmap planning.
• Anticipate future support and maintenance overhead for the data-driven features and models you've built.
• Analyze our data sets to help inform product roadmaps.
• Devise optimization models to recommend ways to improve fraud and compliance workflows.
• Use heuristics, anomaly detection methods, and unsupervised machine learning methods to detect and predict fraud.
• Leverage a deep, data-driven understanding of the key drivers and metrics underpinning Alloy's products and business lines to draw insights and make recommendations that will help the company grow and scale effectively
• Conduct bespoke analyses and research for new customer use cases that support future development of data science products

We’re looking for

You are:

• Always building with end-solution in mind. 
• Able to communicate complicated concepts to a non-technical audience without diluting the complexity of the work.
• Able to build strong cross-functional relationships within Alloy.
• Naturally curious with a knack for asking tough questions.
• A team player. You believe that big things happen when the right people are working together.
• A fast learner
• Humble. Mistakes happen and owning them helps us learn and move on quickly
• An excellent teammate, willing to offer help and advice when needed
• Product-oriented. You have a desire to understand Alloy's business, strategy and priorities to help guide future product development

You have:

• 6 years of relevant experience as a data scientist, conducting advanced analytics and building/iterating on real-world production end-to-end models.
• 2 years experience as a tech lead
• Advanced proficiency in scripting languages like Python and querying languages like SQL
• Experience with classification, clustering, regression, and time series models.
• Experience working with unbalanced data sets and regularization methods.
• Experience building models from scratch, iterating, and owning projects end to end.
• A BA in a quantitative field, or equivalent experience
• You have experience in a highly analytical role in fast-paced environments
• You have a knack for details, and making sure things are correct/accurate

Nice to Haves:

• Professional experience in fraud detection
• Experience maintaining production machine learning models
• Experience with AWS SageMaker
• Prior